IMPACT ASSESSMENT FOR AUTOMATEDDRIVING · Simulation of reconstructed case with ADAS / AD ......

EG-342 | 14th November 2018Dr. Felix Fahrenkrog

IMPACT ASSESSMENT FOR AUTOMATED DRIVINGSIP-ADUS WORKSHOP 2018

Tools

TRAFFIC, ACCIDENTS AND TRAFFIC SAFETY.METHODS FOR SAFETY EVALUATION.

SIP-adus Workshop 2018 | 14th November 2018 | Fahrenkrog, BMW

Field TestTest Track / Simulator Simulation

Accidentswithout System

Accidentswith System

ReducedRisks

AddedRisks

Multi-dimensional Situation Space

Traffic Situations

Tools

SafetyEvaluation

IMPACT ASSESSMENT. METHODOLOGY - ACCIDENT- VS. TRAFFIC-BASED APPROACH.

1) Accident-based

Accident reconstruction

Simulation of reconstructed case with ADAS / AD

Identification of relevant traffic scenarios

ΔSingle-Case

2

Initial constellation

Relevant traffic

scenarios

1

3

1

ΣΔpos- ΣΔneg

3

4Stochastic simulation of traffic situation with ADAS / AD

Stochastic simulation of traffic situation without ADAS / AD

2

2) Traffic-based


FOT & DataAssessment Tools & Data

Impact Assess-ment Method

Impact Assess-ment Tool

TRAFFIC, ACCIDENTS AND TRAFFIC SAFETY.RESEARCH & HARMONIZATION EFFORTS IN SAFETY EVALUATION.

Harmonization Activity

Analyse test tools for (the validation of) automated driving functions.

Provide a common set of relevant situations (database).

Objective

Harmonize / standardize methods for prospective safety performance assessment by virtual simulation of ADAS & AD in order to overcome the issue of variety.

Test of automated driving function on public roads.

Collect data with the automated driving (AD) function.

Provide a transparent software platform that enables the simulation of traffic situations to predict the real-world effectiveness of ADAS and AD.

ResearchActivities


IMPACT ASSESSMENT.METHODOLOGY - P.E.A.R.S.

Representative assessment of active safety and automated driving requires harmonized methods.

For simulation: methods, processes, and models for prospective assessment have to be harmonized.

P.E.A.R.S. is an open working platform to create of a worldwide standard for the evaluation of systems within the pre-crash phase.

WG A “Method, Models and Effectiveness Calculation” WG B “Round Robin Simulation” WG C “Data and Validation & Verification” WG D “ISO and External Communication”

ISO Technical Report 21934-1: ”Road vehicles — Prospective safety performance assessment of pre-crash technology by virtual simulation -- Part 1: State-of-the-art and general method overview“

SIP-adus Workshop 2018 | 14th November 2018 | Fahrenkrog, BMW Seite 5

IMPACT ASSESSMENT.VIRTUAL ASSESSMENT.


Scenario Modelbrakelight (i-1) and visiblebraking?

advance reaction sequence of icar (elapsed ++)

elapsed car i > tbrems?

reaction state icar active

advance position of car using previous speed

YES

YES

rearend collision?

determine acceleration wish of icar

acceleration wish < coasting deceleration?

brakelight icar active

NO

YES

hier quasi entschieden ob „reaction_flag set“?? ja

vehicle data

update speed of icar taking constraints into account

Save accident dataYES

define speed of icar = speed of icar -1

reaction state icar inactive NO

hier quasi entschieden ob „reaction_flag set“??

NO

acute_warning_state?pre_warning ? NO

YES

NO

YESVehicle andFunction Model

determine ttb

iBrake/FCW CCM Warnung

Current warning state

warning state already in effect?

warning state = true, no need to

compute

YES

visibilitydist > distance to object?NO

v > v_object?

YES

B_driver: assumed deceleration of driver

v_egov_objectacc_ego

acc_objectdist_to_object

ttb < threshhold?

Warning alarm = false

NO

Warning alarm = true

YES

NO

NO

iBrake parameter:

v_decrease_iBrake_max zielbremslimit ramptimevsystem prewarningswitch acutewarningswitch zielbremsungswichibswitch1 ibswitch2 ibswitch3 driverBraking_predriverBraking_acute ttbtrigger acc1_ibrake acc2_ibrakeacc_agb wait1 duration1 ramplimitkmh agblimtkmh

compute object stop time

test for crash at ttb_max?

binary search between ttb_min &

ttb_max

YES

compute hypothetical

trajectory

ttb located?

NO

Driver BehaviourModel

Determine minimum buffer distance to

preceeding vehicle

Determine panic distance to preceeding

vehicle

Determine panic distance to preceeding

vehicle

Initialization

Preceeding vehicle visible?

Acceleration wish = 0 NO

Compute perceived distance

(derive from statistical model)

YES

Generate statistical model of perceived

distance

Reaction flag set?

Reaction flag (acute) ?

vi > vdown?

YES

Define acc1 by kinematic formula with perceived_distance:acc1 = -(v-vdown)2 / perceived_distance

Caution: This deceleration can be very strong!!

YES

too close? NO

acc1 = a_coasting (vom Gas gehen)

YES

Normal following: acc1 = min (comfort,

(v-vdown)2 / perceived_distance)

NO

distance to vehicle (i-1) < acceleration perception distance?

Wo ist acceleration perception distance

definiert?

YES

acc2 = minimum (acc1, ai-1)

acc2 = acc1

NO

anticipation flag set? NO

YES

v < v down && too close

v > v down && too close NO

acc1 = coasting

v > v down && not (too close) NO

acc1 = min(-coasting, -(v-vdown)2 / perceived_distance)

acc1 = max(-comfort, -(v-vdown)2 / perceived_distance)

NO

anticipation flag set?

acc2 = acc1 acc2 = min (a_anticipation, acc1)

NO YES

low-pass filter (acc2)

acceleration wish = max(-brakemax and low-pass filter (acc2))

compute anticipation

acceleration from anticipation model

?xi-1< minimum buffer distance?

Draw random numer: probability = samplerate*tick

YES

resample this step?

Probability = probability für Vorausschau (anticipation)?genaue Parametrierung s. Bericht

do over visible downstream cars

YES

NOprevious anticipation

visibility distance

Determine visibility of all preceeding vehicles

vehicle dataperception factor

estimate perceived fictive distance and ?v to each visible downstream

vehicle

minimum acceleration among visible downstream cars

anticipation acceleration

anticipation

anticipation = 0

NO

IMPACT ASSESSMENT.VIRTUAL ASSESSMENT - SIMULATION FRAMEWORK OPENPASS.


OpenPASS is a new software framework for simulation and evaluation of ADAS and automated driving

Join initiative of OEMs (Daimler, VW, Toyota and BMW), Suppliers (Bosch) and other partners (TÜV Süd, itk) with scope of harmonization of simulation tools

Realistic traffic models and simulation investigate interaction between different traffic participants

Fast and efficient simulation consider a high number of situations

Open source approach generate trust and acceptance by authorities and public (Eclipse project: sim@OpenPASS)

BMW VisualizationBMW Visualization

IMPACT ASSESSMENT. IDENTIFICATION OF TOP-SCENARIOS FOR AUTOMATED DRIVING.

Accident data (e.g. GIDAS) / Critical situations (FOT)

Based on specification of function Virtual FOT

Top Scenarios

Simulation of traffic scenarios

Relevant Scenarios

A B C


RESULTS FROM EU RESEARCH PROJECT ADAPTIVE.OBSTACLE IN THE LANE.

Obstacle in the lane

Scenario Conditions

Probability of remaining crash-free [-]

Parameter ValueSCMDriver

ADF Delta (absolute)

Delta (relative)

Overall - 29.9% 58.2% -28.3% -48,6%

Traffic volume

900veh./h 30.9% 61.6% -30.5% -49,8%

1200 veh./h 28.9% 54.8% -25.9% -47,3%

The survival function


Simulation Time [s]

Pro

porti

on o

f sim

ulat

ion

w/o

col

lisio

n [s

]

RESULTS FROM RESEARCH PROJECT KO-HAF.“MINIMUM RISK MANOEUVRE”.


Analyse the consequences of two artificial Minimum Risk Manoeuvre (MRM) that consider only braking in the lane (moderate braking vs. strong braking).

The effect of the MRM was simulated for 13 conditional variations in partial factorial design considering the following parameters: speed limit, traffic density and penetration rate of cooperative automated vehicles.

RESULTS FROM RESEARCH PROJECT KO-HAF.“MINIMUM RISK MANOEUVRE”.


A lower deceleration while the MRM seems to be beneficial in terms of traffic safety benefits, however it requires a longer operation of the system.

V2V communication shows a benefits in the simulation, however larger effects are observed at high penetration rates (>75 %).

0

0.0004

0.0008

0.0012

0 20 40 60 80 100

Col

lisio

n R

ate

[1/k

m]

Penetration rate of passenger cars with cooperative HAD system [%]

Baseline w/o HAD & MRM MRM w. Moderate DecelerationMRM w. Strong Deceleration

*: The case with 100% penetration rate of the passenger cars with a cooperative HAD represents a pure theoretic case that is not realistic.

*

*

OUTLOOK: SAFETY IMPACT ASSESSMENT IN L3PILOT:


Function description

Identify driving scenarios with potentially reduced

risks (accident scenarios)

Identify driving scenarios with potentially added risks

Definition of to be analysed(relevant) driving scenario

Simulation of relevant driving scenario

Safety effect per driving Scenario (frequency of

accident, collision velocity / position)

Simulation of relevant traffic scenario

Definition traffic scenarios (country specific)

Traffic & infrastructure data Accident data

Change of frequency of driving scenarios at

different penetration rates

Determine accident severity (Injuries) per

driving scenario

Safety impact per driving scenario and penetration

rate

Scen

ario

D

efin

ition

Det

erm

ine

Effe

ctIn

put

Dat

aU

p-sc

alin

g

Input from L3Pilot in-depth evaluation

Identify ODD covered driving

scenario (country specific)

ERiC Method

Weighting factors per driving scenario / country

Incl. Parameters for scenarios, input to modells etc.

The safety impact assessment in L3Pilot aims at:

- Bring together the knowledge and method(s) from different projects and partners.

- Comprehensive assessment of driving scenarios with expected positive and negative effects.

- Consider information about personal attitude towards automated driving from annual survey (e.g. usage).

- Assess the effect for a larger region (aiming at Europe).

- Consider data from the real world pilot tests from different regions in Europe.

PROCESS AND ROLES.VISION OF ACTIVE SAFETY EVALUATION.

Neutral (scientific) institutions

Accidentdata

Trafficdata

Humanfactor data

Stochastic scenarios

Industry

Simulationand analysis Results

AD / ADASmodel

Test institute

Weightingof simulated

resultsResults

Caseselection

Spottesting

Rating



THANK YOU

IMPACT ASSESSMENT FOR AUTOMATEDDRIVING · Simulation of reconstructed case with ADAS / AD ......

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Transcript of IMPACT ASSESSMENT FOR AUTOMATEDDRIVING · Simulation of reconstructed case with ADAS / AD ......